Global gridded near-surface wind speed dataset on a monthly scale (1973-2021)- A Big Earth Data Platform for Three Poles

Wind speed data is widely used in many sciences, management, and policy fields to assess renewable energy potential, address wind hazards, investigate biological phenomena, and explore climate change/variability, among other applications. The challenge is obtaining complete and accurate wind datasets, as observations are limited in distribution. Global-scale weather stations suffer from spatial and temporal discontinuities that limit their utility. While reanalysis products and climate model simulations achieve data continuity, they often fail to reproduce significant wind speed trends because few of them assimilate in-situ wind observations on land. Data interpolation helps fill gaps, but the high variability of wind speed data, combined with a low distribution of observations worldwide, prevents standard statistical interpolation methods such as kriging or principal component analysis from being accurate for areas with sparse data. As a result, wind speed data has been the bottleneck in related studies.

Here, based on the partial convolutional neural network, we reconstructed the global near-surface wind speed data during 1973-2021 by assimilating simulation outputs from 34 climate models and the HadISD dataset, which the Met Office Hadley Center creates. Our dataset has a spatial resolution of 1.25°×2.5° and containers observed wind speed trends.

File naming and required software

The data is stored in a NetCDF file named GGWS-PCNN wind_ speed-yyyymmyyyymm_ vX.nc. With "GGWS-PCNN wind_ speed-197301202012_ v312202105p. nc" as an example, "GGFS-PCNN" is the English abbreviation of the dataset (from the global gridded monthly wind speed dataset by the partial volatile neural network), "wind_ speed" indicates that the dataset stores wind speed variables, "197301202012" denotes that the period of the data is from Jan. 1973 to Dec. 2020, and v312202105p represents that the dataset uses HadISD data with the version of v312202105p. The data can be processed by Matlab, ArcGIS and other software.

Notice: Due to the limitations of existing AI algorithms in reconstructing data with many missing values, our product has a small number of outliers (e.g. wind speeds less than zero or very high), most of which are located in the Antarctic region. We recommend you remove these outliers before using this dataset.

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Cite as:

Zhou, L., Zeng, Z., Jiang, X. (2022). Global gridded near-surface wind speed dataset on a monthly scale (1973-2021). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Atmos.tpdc.272893. CSTR: 18406.11.Atmos.tpdc.272893. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Zhou, L.H., Liu., H.F., Jiang, X., Ziegler, A.D., Azorin-Molina, C., Liu, J., & Zeng, Z.Z. (2022). An artificial intelligence reconstruction of global gridded surface winds. Science Bulletin, online.( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Copyright & License

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License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)